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        data-chapter-title="Python数据课程 软件和环境安装"
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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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        <li class="chapter " data-level="4.4.6" data-path="数据分析库的操作/10Pandas数据规整-清理.html">
            
                
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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        <li class="chapter " data-level="4.4.8" data-path="数据分析库的操作/16Pandas数据规整-合并.html">
            
                
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
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        <li class="chapter " data-level="4.5" data-path="数据分析库的操作/13Pandas数据规整-重塑和轴向旋转.html">
            
                
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
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        <li class="chapter " data-level="4.5.1" data-path="数据分析库的操作/13.1透视表和交叉表.html">
            
                
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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                            <b>5.7.</b>
                        
                        拓展：pylab
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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<li><p>&#x5B89;&#x88C5;&#x6CE8;&#x610F;&#xFF1A;</p>
<ol>
<li>&#x7528;&#x7BA1;&#x7406;&#x5458;&#x7528;&#x6237;&#x767B;&#x5F55;&#x64CD;&#x4F5C;&#x7CFB;&#x7EDF;&#x5B89;&#x88C5;&#x8F6F;&#x4EF6;</li>
<li>&#x5B89;&#x88C5;&#x8DEF;&#x5F84;&#xFF1A;&#x4FEE;&#x6539;&#x76D8;&#x7B26;(&#x4E0D;&#x8981;&#x88C5;&#x5728;C&#x76D8;&#xFF0C;&#x6743;&#x9650;&#x95EE;&#x9898;)&#xFF0C;&#x5EFA;&#x8BAE;&#x4E0D;&#x8981;&#x4FEE;&#x6539;&#x5176;&#x4ED6;&#x8DEF;&#x5F84;&#xFF08;&#x8DEF;&#x5F84;&#x4E0D;&#x80FD;&#x5E26;&#x7A7A;&#x683C;&#x548C;&#x4E2D;&#x6587;&#xFF09;&#x3002;&#x5982;&#x5C06;&#x5B89;&#x88C5;&#x8DEF;&#x5F84;&#x6539;&#x4E3A;e:\Anaconda3</li>
<li>&#x5EFA;&#x8BAE;&#x9009;&#x4E2D;&#xFF1A;<ul>
<li>add anaconda to the system path environment variable</li>
<li>&#xFF08;&#x5C06;anaconda&#x6DFB;&#x52A0;&#x5230;&#x7CFB;&#x7EDF;&#x8DEF;&#x5F84;&#x73AF;&#x5883;&#x53D8;&#x91CF;&#xFF09;</li>
<li>&#x8FD9;&#x6837;&#x7CFB;&#x7EDF;&#x7EC8;&#x7AEF;&#x4E5F;&#x80FD;&#x6267;&#x884C;Anaconda&#x547D;&#x4EE4;&#xFF0C;&#x5426;&#x5219;&#x53EA;&#x6709;Anaconda&#x81EA;&#x5E26;&#x7684;&#x7EC8;&#x7AEF;&#x731C;&#x80FD;&#x6267;&#x884C;&#x547D;&#x4EE4;</li>
</ul>
</li>
<li>&#x5EFA;&#x8BAE;&#x9009;&#x4E2D;&#xFF1A;
register anaconda as the system python3.7
&#xFF08;&#x5C06;Anaconda&#x6CE8;&#x518C;&#x4E3A;&#x7CFB;&#x7EDF;python3.7&#xFF09;
&#x8FD9;&#x6837;&#x5176;&#x4ED6;Python&#x7F16;&#x8F91;&#x5668;&#x3001;IDE&#x53EF;&#x4EE5;&#x627E;&#x5230;&#x5E76;&#x4F7F;&#x7528;Anaconda&#x7684;Python&#x89E3;&#x91CA;&#x5668;</li>
<li>&#x6700;&#x540E;&#xFF0C;&#x9009;&#x62E9;&#x662F;&#x5426;&#x5B89;&#x88C5; VSCode, skip&#x8DF3;&#x8FC7;<ul>
<li><h6 id="&#x6CE8;&#x610F;&#xFF1A;&#x9009;&#x4E2D;&#x6B64;&#x9879;&#x540E;&#xFF0C;anaconda&#x81EA;&#x5E26;&#x7684;python&#x89E3;&#x91CA;&#x5668;&#x4F1A;&#x8986;&#x76D6;&#x7CFB;&#x7EDF;&#x539F;&#x6765;&#x5B89;&#x88C5;&#x7684;python&#xFF0C;&#x89E3;&#x51B3;&#x65B9;&#x6CD5;&#x89C1;&#x540E;">&#x6CE8;&#x610F;&#xFF1A;&#x9009;&#x4E2D;&#x6B64;&#x9879;&#x540E;&#xFF0C;Anaconda&#x81EA;&#x5E26;&#x7684;Python&#x89E3;&#x91CA;&#x5668;&#x4F1A;&#x8986;&#x76D6;&#x7CFB;&#x7EDF;&#x539F;&#x6765;&#x5B89;&#x88C5;&#x7684;Python&#xFF0C;&#x89E3;&#x51B3;&#x65B9;&#x6CD5;&#x89C1;&#x540E;</h6>
</li>
</ul>
</li>
</ol>
</li>
</ul>
</li>
</ul>
<hr>
<ul>
<li><p>&#x68C0;&#x67E5;Anaconda&#x73AF;&#x5883;&#x662F;&#x5426;&#x5B89;&#x88C5;&#x6210;&#x529F;&#xFF1A;</p>
<ol>
<li>&#x5FEB;&#x6377;&#x952E;win+R&#xFF0C;&#x8F93;&#x5165;cmd&#x56DE;&#x8F66;&#xFF0C;&#x6253;&#x5F00;&#x7CFB;&#x7EDF;&#x7EC8;&#x7AEF;</li>
<li>&#x8F93;&#x5165;&#x547D;&#x4EE4;python -V&#xFF0C;&#x67E5;&#x770B;&#x4FE1;&#x606F;</li>
<li>&#x8F93;&#x5165;&#x547D;&#x4EE4;conda -V&#xFF0C;&#x67E5;&#x770B;&#x4FE1;&#x606F;</li>
</ol>
</li>
</ul>
<hr>
<ul>
<li><p>chrome&#x6D4F;&#x89C8;&#x5668;</p>
<ul>
<li>&#x8BBE;&#x4E3A;&#x9ED8;&#x8BA4;&#x6D4F;&#x89C8;&#x5668;</li>
</ul>
</li>
<li><p>VSCode&#x6587;&#x672C;&#x7F16;&#x8F91;&#x5668;&#xFF1A;&#x5B89;&#x88C5;&#x540E;&#x9700;&#x8054;&#x7F51;&#x4E0B;&#x8F7D;&#x51E0;&#x4E2A;&#x63D2;&#x4EF6;,&#x63D2;&#x4EF6;&#x9762;&#x677F;&#x641C;&#x7D22;&#xFF1A;</p>
<ul>
<li>Chinese ...&#xFF1A;&#x4E2D;&#x6587;&#x7B80;&#x4F53;&#x8BED;&#x8A00;&#x5305;</li>
<li>Markdown Preview Enhanced&#xFF1A;markdown&#x9884;&#x89C8;</li>
<li>Python &#xFF1A;&#x6267;&#x884C;Python&#x7A0B;&#x5E8F;&#xFF08;&#x9700;&#x5B89;&#x88C5;Python&#x73AF;&#x5883;&#xFF09;</li>
</ul>
</li>
</ul>
<hr>
<h4 id="&#x6253;&#x5F00;&#x7CFB;&#x7EDF;&#x7EC8;&#x7AEF;&#x4E24;&#x79CD;&#x65B9;&#x5F0F;">&#x6253;&#x5F00;&#x7CFB;&#x7EDF;&#x7EC8;&#x7AEF;&#x4E24;&#x79CD;&#x65B9;&#x5F0F;</h4>
<ul>
<li>windows+r - cmd - &#x56DE;&#x8F66;<ul>
<li>&#x8FDB;&#x5165;&#x5F53;&#x524D;&#x7528;&#x6237;&#x7CFB;&#x7EDF;&#x76EE;&#x5F55;</li>
</ul>
</li>
<li>&#x8FDB;&#x5165;&#x76EE;&#x5F55;&#xFF0C;shift&#x952E;&#xFF0C;&#x9F20;&#x6807;&#x53F3;&#x952E;&#xFF0C;&#x6253;&#x5F00;&#x7EC8;&#x7AEF;<ul>
<li>&#x8FDB;&#x5165;&#x672C;&#x76EE;&#x5F55;</li>
</ul>
</li>
</ul>
<hr>
<h4 id="&#x6D4B;&#x8BD5;anaconda&#x73AF;&#x5883;&#x662F;&#x5426;&#x6B63;&#x5E38;">&#x6D4B;&#x8BD5;Anaconda&#x73AF;&#x5883;&#x662F;&#x5426;&#x6B63;&#x5E38;</h4>
<p>&#x8FDB;&#x5165;&#x7EC8;&#x7AEF;&#xFF0C;&#x8F93;&#x5165;&#x547D;&#x4EE4;</p>
<pre><code class="lang-bash">python -V
conda -V

d:
<span class="hljs-built_in">cd</span> Python12/<span class="hljs-number">01</span>
dir

jupyter notebook
</code></pre>
<p>&#x5982;&#x679C;&#x6267;&#x884C;<code>jupyter notebook</code>&#x4E0D;&#x81EA;&#x52A8;&#x5F39;&#x51FA;Chrome&#x6D4F;&#x89C8;&#x5668;&#xFF0C;&#x6216;&#x8005;&#x5F39;&#x51FA;&#x5176;&#x4ED6;&#x6D4F;&#x89C8;&#x5668;&#xFF0C;&#x53EF;&#x4EE5;&#x590D;&#x5236;&#x7EC8;&#x7AEF;&#x7684;URL&#x76F4;&#x63A5;&#x7C98;&#x8D34;&#x5230;Chrome&#x6D4F;&#x89C8;&#x5668;&#x8BBF;&#x95EE;&#x7F51;&#x9875;</p>
<ul>
<li>&#x7EC8;&#x7AEF;&#x53F3;&#x952E; - &#x6807;&#x8BB0;</li>
<li>&#x9F20;&#x6807;&#x4ECE;&#x6700;&#x5DE6;&#x4FA7;&#x5230;&#x6700;&#x53F3;&#x4FA7;&#x9009;&#x4E2D;URL&#xFF0C;&#x56DE;&#x8F66;&#x590D;&#x5236;</li>
<li>Chrome&#x6D4F;&#x89C8;&#x5668;&#x4E0A;&#x7C98;&#x8D34;URL&#x56DE;&#x8F66;&#x8BBF;&#x95EE;</li>
</ul>

                    
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